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# UniDic2UD

Tokenizer, POS-tagger, lemmatizer, and dependency-parser for modern and contemporary Japanese, working on [Universal Dependencies](https://universaldependencies.org/format.html).

## Basic usage

```py
>>> import unidic2ud
>>> qkana=unidic2ud.load("qkana")
>>> s=qkana("其國を治めんと欲する者は先づ其家を齊ふ")
>>> print(s)
# text = 其國を治めんと欲する者は先づ其家を齊ふ
1	其	其の	DET	連体詞	_	2	det	_	SpaceAfter=No|Translit=ソノ
2	國	国	NOUN	名詞-普通名詞-一般	_	4	obj	_	SpaceAfter=No|Translit=クニ
3	を	を	ADP	助詞-格助詞	_	2	case	_	SpaceAfter=No|Translit=ヲ
4	治め	収める	VERB	動詞-一般	_	7	advcl	_	SpaceAfter=No|Translit=オサメ
5	ん	む	AUX	助動詞	_	4	aux	_	SpaceAfter=No|Translit=ン
6	と	と	ADP	助詞-格助詞	_	4	case	_	SpaceAfter=No|Translit=ト
7	欲する	欲する	VERB	動詞-一般	_	8	acl	_	SpaceAfter=No|Translit=ホッスル
8	者	者	NOUN	名詞-普通名詞-一般	_	14	nsubj	_	SpaceAfter=No|Translit=モノ
9	は	は	ADP	助詞-係助詞	_	8	case	_	SpaceAfter=No|Translit=ハ
10	先づ	先ず	ADV	副詞	_	14	advmod	_	SpaceAfter=No|Translit=マヅ
11	其	其の	DET	連体詞	_	12	det	_	SpaceAfter=No|Translit=ソノ
12	家	家	NOUN	名詞-普通名詞-一般	_	14	obj	_	SpaceAfter=No|Translit=ウチ
13	を	を	ADP	助詞-格助詞	_	12	case	_	SpaceAfter=No|Translit=ヲ
14	齊ふ	整える	VERB	動詞-一般	_	0	root	_	SpaceAfter=No|Translit=トトノフ

>>> t=s[7]
>>> print(t.id,t.form,t.lemma,t.upos,t.xpos,t.feats,t.head.id,t.deprel,t.deps,t.misc)
7 欲する 欲する VERB 動詞-一般 _ 8 acl _ SpaceAfter=No|Translit=ホッスル

>>> print(s.to_tree())
    其 <══╗         det(決定詞)
    國 ═╗═╝<╗       obj(目的語)
    を <╝   ║       case(格表示)
  治め ═╗═╗═╝<╗     advcl(連用修飾節)
    ん <╝ ║   ║     aux(動詞補助成分)
    と <══╝   ║     case(格表示)
欲する ═══════╝<╗   acl(連体修飾節)
    者 ═╗═══════╝<╗ nsubj(主語)
    は <╝         ║ case(格表示)
  先づ <══════╗   ║ advmod(連用修飾語)
    其 <══╗   ║   ║ det(決定詞)
    家 ═╗═╝<╗ ║   ║ obj(目的語)
    を <╝   ║ ║   ║ case(格表示)
  齊ふ ═════╝═╝═══╝ root(親)

>>> f=open("trial.svg","w")
>>> f.write(s.to_svg())
>>> f.close()
```
![trial.svg](https://raw.githubusercontent.com/KoichiYasuoka/UniDic2UD/master/trial.png)

`unidic2ud.load(UniDic,UDPipe)` loads a natural language processor pipeline, which uses `UniDic` for tokenizer POS-tagger and lemmatizer, then uses `UDPipe` for dependency-parser. The default `UDPipe` is `UDPipe="japanese-modern"`. Available `UniDic` options are:

* `UniDic="gendai"`: Use [現代書き言葉UniDic](https://unidic.ninjal.ac.jp/download#unidic_bccwj).
* `UniDic="spoken"`: Use [現代話し言葉UniDic](https://unidic.ninjal.ac.jp/download#unidic_csj).
* `UniDic="qkana"`: Use [旧仮名口語UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_qkana).
* `UniDic="kindai"`: Use [近代文語UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_kindai).
* `UniDic="kinsei"`: Use [近代口語（洒落本）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_kinsei).
* `UniDic="kyogen"`: Use [中世口語（狂言）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_kyogen).
* `UniDic="wakan"`: Use [中世文語（説話・随筆）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_wakan).
* `UniDic="wabun"`: Use [中古和文UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_wabun).
* `UniDic="manyo"`: Use [上代（万葉集）UniDic](https://unidic.ninjal.ac.jp/download_all#unidic_manyo).
* `UniDic=None`: Use `UDPipe` for tokenizer, POS-tagger, lemmatizer, and dependency-parser.

`unidic2ud.UniDic2UDEntry.to_tree()` has an option `to_tree(BoxDrawingWidth=2)` for old terminals, whose Box Drawing characters are "fullwidth".

You can simply use `unidic2ud` on the command line:
```sh
echo 其國を治めんと欲する者は先づ其家を齊ふ | unidic2ud -U qkana
```

## CaboCha emulator usage

```py
>>> import unidic2ud.cabocha as CaboCha
>>> qkana=CaboCha.Parser("qkana")
>>> s=qkana.parse("其國を治めんと欲する者は先づ其家を齊ふ")
>>> print(s.toString(CaboCha.FORMAT_TREE_LATTICE))
  其-D
  國を-D
治めんと-D
    欲する-D
        者は-------D
          先づ-----D
              其-D |
              家を-D
                齊ふ
EOS
* 0 1D 0/0 0.000000
其	連体詞,*,*,*,*,*,其の,ソノ,*,DET	1<-det-2
* 1 2D 0/1 0.000000
國	名詞,普通名詞,一般,*,*,*,国,クニ,*,NOUN	2<-obj-4
を	助詞,格助詞,*,*,*,*,を,ヲ,*,ADP	3<-case-2
* 2 3D 0/1 0.000000
治め	動詞,一般,*,*,*,*,収める,オサメ,*,VERB	4<-advcl-7
ん	助動詞,*,*,*,*,*,む,ン,*,AUX	5<-aux-4
と	助詞,格助詞,*,*,*,*,と,ト,*,ADP	6<-case-4
* 3 4D 0/0 0.000000
欲する	動詞,一般,*,*,*,*,欲する,ホッスル,*,VERB	7<-acl-8
* 4 8D 0/1 0.000000
者	名詞,普通名詞,一般,*,*,*,者,モノ,*,NOUN	8<-nsubj-14
は	助詞,係助詞,*,*,*,*,は,ハ,*,ADP	9<-case-8
* 5 8D 0/0 0.000000
先づ	副詞,*,*,*,*,*,先ず,マヅ,*,ADV	10<-advmod-14
* 6 7D 0/0 0.000000
其	連体詞,*,*,*,*,*,其の,ソノ,*,DET	11<-det-12
* 7 8D 0/1 0.000000
家	名詞,普通名詞,一般,*,*,*,家,ウチ,*,NOUN	12<-obj-14
を	助詞,格助詞,*,*,*,*,を,ヲ,*,ADP	13<-case-12
* 8 -1D 0/0 0.000000
齊ふ	動詞,一般,*,*,*,*,整える,トトノフ,*,VERB	14<-root
EOS
```
`CaboCha.Parser(UniDic)` is an alias for `unidic2ud.load(UniDic,UDPipe="japanese-modern")`, and its default is `UniDic=None`. `CaboCha.Tree.toString(format)` has five available formats:
* `CaboCha.FORMAT_TREE`: tree (numbered as 0)
* `CaboCha.FORMAT_LATTICE`: lattice (numbered as 1)
* `CaboCha.FORMAT_TREE_LATTICE`: tree + lattice (numbered as 2)
* `CaboCha.FORMAT_XML`: XML (numbered as 3)
* `CaboCha.FORMAT_CONLL`: Universal Dependencies CoNLL-U (numbered as 4)

You can simply use `udcabocha` on the command line:
```sh
echo 其國を治めんと欲する者は先づ其家を齊ふ | udcabocha -U qkana -f 2
```
`-U UniDic` specifies `UniDic`. `-f format` specifies the output format in 0 to 4 above (default is `-f 0`) and in 5 to 8 below:
* `-f 5`: `to_tree()`
* `-f 6`: `to_tree(BoxDrawingWidth=2)`
* `-f 7`: `to_svg()`
* `-f 8`: [raw DOT](https://graphviz.readthedocs.io/en/stable/manual.html#using-raw-dot) graph through [Immediate Catena Analysis](https://koichiyasuoka.github.io/deplacy/#deplacydot)

![dot.png](https://raw.githubusercontent.com/KoichiYasuoka/UniDic2UD/master/dot.png)

Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/UniDic2UD/blob/master/udcabocha.ipynb) for Google Colaboratory.

## Usage via spaCy

If you have already installed [spaCy](https://pypi.org/project/spacy/) 2.1.0 or later, you can use `UniDic` via spaCy Language pipeline.

```py
>>> import unidic2ud.spacy
>>> qkana=unidic2ud.spacy.load("qkana")
>>> d=qkana("其國を治めんと欲する者は先づ其家を齊ふ")
>>> print(type(d))
<class 'spacy.tokens.doc.Doc'>
>>> print(unidic2ud.spacy.to_conllu(d))
# text = 其國を治めんと欲する者は先づ其家を齊ふ
1	其	其の	DET	連体詞	_	2	det	_	SpaceAfter=No|Translit=ソノ
2	國	国	NOUN	名詞-普通名詞-一般	_	4	obj	_	SpaceAfter=No|Translit=クニ
3	を	を	ADP	助詞-格助詞	_	2	case	_	SpaceAfter=No|Translit=ヲ
4	治め	収める	VERB	動詞-一般	_	7	advcl	_	SpaceAfter=No|Translit=オサメ
5	ん	む	AUX	助動詞	_	4	aux	_	SpaceAfter=No|Translit=ン
6	と	と	ADP	助詞-格助詞	_	4	case	_	SpaceAfter=No|Translit=ト
7	欲する	欲する	VERB	動詞-一般	_	8	acl	_	SpaceAfter=No|Translit=ホッスル
8	者	者	NOUN	名詞-普通名詞-一般	_	14	nsubj	_	SpaceAfter=No|Translit=モノ
9	は	は	ADP	助詞-係助詞	_	8	case	_	SpaceAfter=No|Translit=ハ
10	先づ	先ず	ADV	副詞	_	14	advmod	_	SpaceAfter=No|Translit=マヅ
11	其	其の	DET	連体詞	_	12	det	_	SpaceAfter=No|Translit=ソノ
12	家	家	NOUN	名詞-普通名詞-一般	_	14	obj	_	SpaceAfter=No|Translit=ウチ
13	を	を	ADP	助詞-格助詞	_	12	case	_	SpaceAfter=No|Translit=ヲ
14	齊ふ	整える	VERB	動詞-一般	_	0	root	_	SpaceAfter=No|Translit=トトノフ

>>> t=d[6]
>>> print(t.i+1,t.orth_,t.lemma_,t.pos_,t.tag_,t.head.i+1,t.dep_,t.whitespace_,t.norm_)
7 欲する 欲する VERB 動詞-一般 8 acl  ホッスル
```

## Installation for Linux

Tar-ball is available for Linux, and is installed by default when you use `pip`:
```sh
pip install unidic2ud
```

By default installation, `UniDic` is invoked through Web APIs. If you want to invoke them locally and faster, you can download `UniDic` which you use just as follows:
```sh
python -m unidic2ud download qkana
python -m unidic2ud dictlist
```
Licenses of dictionaries and models are: GPL/LGPL/BSD for `gendai` and `spoken`; CC BY-NC-SA 4.0 for others.

## Installation for Cygwin

Make sure to get `gcc-g++` `python37-pip` `python37-devel` packages, and then:
```sh
pip3.7 install unidic2ud
```
Use `python3.7` command in [Cygwin](https://www.cygwin.com/install.html) instead of `python`.

## Installation for Jupyter Notebook (Google Colaboratory)

```py
!pip install unidic2ud
```

## Author

Koichi Yasuoka (安岡孝一)

## References

* 安岡孝一: [漢日英Universal Dependencies平行コーパスとその差異](http://hdl.handle.net/2433/245218), 人文科学とコンピュータシンポジウム「じんもんこん2019」論文集 (2019年12月), pp.43-50.
* Koichi Yasuoka: [Universal Dependencies Parallel Corpora on Classical Chinese, Modern Japanese, and Modern English](http://hdl.handle.net/2433/245218). Jinmoncom 2019: IPSJ Symposium Series, Vol.2019, No.1 (December 2019), pp.43-50.
